Tissue specificity plays an important role in genetic risk prediction. Integrating more functional annotations with higher tissue and cell type specificity may further increase risk prediction accuracy, especially when the tissue type that is biologically relevant to the disease is not well characterized by the seven available tissue tracks in our current analyses. To explore how these factors will affect the AnnoPred model, we performed a few follow-up analyses. We have recently expanded our GenoSkyline annotations to more than 100 tissue and cell types from the Roadmap Epigenomics Project [29]. We investigated the performance of AnnoPred after integrating 66 annotation tracks representing a spectrum of adult tissue and cell types. As shown in S10 Table, incorporating more annotations into the model does not always further improve risk prediction accuracy compared with AnnoPred with fewer annotations in the model. This may be due to the overlap between functional regions (e.g. functional annotations for slightly different brain regions) when incorporating too many annotation tracks into the model, which will cause numerically unstable heritability estimates. This is because annotation-stratified LD score regression, the
